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Data processing of physiological sensor data and alarm determination utilising activity recognition

Kang, James Jin, Luan, Tom H and Larkin, Henry 2016, Data processing of physiological sensor data and alarm determination utilising activity recognition, International journal of information, communication technology and applications, vol. 2, no. 1, pp. 108-131, doi: 10.17972/ijicta20162132.

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Title Data processing of physiological sensor data and alarm determination utilising activity recognition
Author(s) Kang, James JinORCID iD for Kang, James Jin orcid.org/0000-0002-0242-4187
Luan, Tom H
Larkin, HenryORCID iD for Larkin, Henry orcid.org/0000-0001-5867-1542
Journal name International journal of information, communication technology and applications
Volume number 2
Issue number 1
Start page 108
End page 131
Total pages 24
Publisher Australasian Association for Information and Communication Technology
Place of publication Melbourne, Vic.
Publication date 2016-09-24
ISSN 2205-0930
Keyword(s) body sensors
WBAN
IoT
activity recognition
inference
Summary Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.
Language eng
DOI 10.17972/ijicta20162132
Field of Research 080702 Health Informatics
100504 Data Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Australasian Association for Information and Communication Technology
Free to Read? Yes
Use Rights Creative Commons Attribution non-commercial licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086335

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.